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pkgs <-  c("tidyverse","here", "lmerTest", "sjPlot","broom.mixed", "kableExtra", "ggeffects", "gt", "brms", "bayestestR","ggdist", "pheatmap", "heatmaply","pheatmap","gplots","RColorBrewer", "tm", "wordcloud", "psych")
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here::i_am("Analysis/idmPrelimAnal.Rmd")
## here() starts at /Users/jacobelder/Documents/GitHub/EpMemNet
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/corToOne.R")
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fullLong <- arrow::read_parquet(here("Data", "longEpMNet.parquet"))
fullShort <- arrow::read_parquet(here("Data","shortEpMNet.parquet"))
fullLong$subID <- as.numeric(fullLong$subID)
fullData <- fullLong %>% full_join(fullShort, by = c("subID"))

Descriptives

# How many people listed 0 connections?
nrow(fullShort[which(fullShort$edgeTot==0),])
## [1] 19
#describe(fullLong$strength)

Wordcloud

#Create a vector containing only the text
text <- as.vector(fullData$memory)
# Create a corpus  
docs <- Corpus(VectorSource(text))
docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
## Warning in tm_map.SimpleCorpus(., removeNumbers): transformation drops documents
## Warning in tm_map.SimpleCorpus(., removePunctuation): transformation drops
## documents
## Warning in tm_map.SimpleCorpus(., stripWhitespace): transformation drops
## documents
docs <- tm_map(docs, content_transformer(tolower))
## Warning in tm_map.SimpleCorpus(docs, content_transformer(tolower)):
## transformation drops documents
docs <- tm_map(docs, removeWords, stopwords("english"))
## Warning in tm_map.SimpleCorpus(docs, removeWords, stopwords("english")):
## transformation drops documents
docs <- tm_map(docs, removeWords, c("the","and"))
## Warning in tm_map.SimpleCorpus(docs, removeWords, c("the", "and")):
## transformation drops documents
dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df <- data.frame(word = names(words),freq=words)

set.seed(24)
wordcloud(words = df$word, freq = df$freq, min.freq = 1,           max.words=200, random.order=FALSE, rot.per=0.35,            colors=brewer.pal(8, "Dark2"))

Sanity Checks

Does time predict number of causes?

Yes, the farther away in time, the more experiences something causes.

m<-glmer(outdegree ~  scale(length) + numID + ( scale(length) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: outdegree ~ scale(length) + numID + (scale(length) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7829.3   7863.5  -3908.7   7817.3     2209 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3004 -0.9114 -0.1534  0.5892 11.2174 
## 
## Random effects:
##  Groups Name          Variance Std.Dev. Corr 
##  subID  (Intercept)   0.23383  0.4836        
##         scale(length) 0.08941  0.2990   -0.13
## Number of obs: 2215, groups:  subID, 215
## 
## Fixed effects:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -0.283669   0.077167  -3.676 0.000237 ***
## scale(length)  0.114670   0.035697   3.212 0.001317 ** 
## numID          0.038627   0.004514   8.557  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(l)
## scal(lngth) -0.088       
## numID       -0.816  0.054

Does time predict causes of experience?

Yes, the farther back in time, the fewer experiences cause something.

m<-glmer(indegree ~  scale(length) + numID + ( scale(length) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: indegree ~ scale(length) + numID + (scale(length) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7458.3   7492.5  -3723.1   7446.3     2209 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9442 -0.6458 -0.1297  0.2974  9.5454 
## 
## Random effects:
##  Groups Name          Variance Std.Dev. Corr 
##  subID  (Intercept)   0.26312  0.5130        
##         scale(length) 0.07689  0.2773   -0.25
## Number of obs: 2215, groups:  subID, 215
## 
## Fixed effects:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -0.185839   0.078373  -2.371   0.0177 *  
## scale(length) -0.147931   0.037409  -3.954 7.67e-05 ***
## numID          0.033445   0.004683   7.142 9.20e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(l)
## scal(lngth)  0.004       
## numID       -0.809 -0.027

H1: People will evaluate more positively, less negatively (i.e., more favorably) on memories with more downstram dependents.

Valence Self-Report

More positive and negative, experience causes more experience

More positive, experience is caused by more experiences

m<-glmer(outdegree ~  scale(positive) + scale(negative) + numID + ( scale(positive) + scale(negative) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: 
## outdegree ~ scale(positive) + scale(negative) + numID + (scale(positive) +  
##     scale(negative) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   5185.0   5237.5  -2582.5   5165.0     1394 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.1054 -0.9130 -0.1103  0.6085  7.7908 
## 
## Random effects:
##  Groups Name            Variance Std.Dev. Corr       
##  subID  (Intercept)     0.16785  0.4097              
##         scale(positive) 0.05166  0.2273   -0.07      
##         scale(negative) 0.04591  0.2143   -0.20  0.83
## Number of obs: 1404, groups:  subID, 204
## 
## Fixed effects:
##                  Estimate Std. Error z value Pr(>|z|)    
## (Intercept)     -0.096675   0.076445  -1.265  0.20600    
## scale(positive)  0.136995   0.044201   3.099  0.00194 ** 
## scale(negative)  0.152951   0.046860   3.264  0.00110 ** 
## numID            0.039450   0.004358   9.053  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(p) scl(n)
## scale(pstv)  0.151              
## scale(ngtv)  0.137  0.771       
## numID       -0.816 -0.026 -0.040
m<-glmer(indegree ~  scale(positive) + scale(negative) + numID + ( scale(positive) + scale(negative) | subID), data=fullData,family="poisson")
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0114173 (tol = 0.002, component 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: 
## indegree ~ scale(positive) + scale(negative) + numID + (scale(positive) +  
##     scale(negative) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   5028.9   5081.4  -2504.5   5008.9     1394 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3588 -0.6642 -0.1111  0.3082  8.6045 
## 
## Random effects:
##  Groups Name            Variance Std.Dev. Corr     
##  subID  (Intercept)     0.238347 0.48821           
##         scale(positive) 0.006408 0.08005  0.22     
##         scale(negative) 0.067935 0.26064  0.23 1.00
## Number of obs: 1404, groups:  subID, 204
## 
## Fixed effects:
##                  Estimate Std. Error z value Pr(>|z|)    
## (Intercept)     -0.121492   0.081602  -1.489 0.136533    
## scale(positive)  0.138182   0.039441   3.504 0.000459 ***
## scale(negative)  0.056178   0.051405   1.093 0.274466    
## numID            0.035139   0.004771   7.364 1.78e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(p) scl(n)
## scale(pstv)  0.145              
## scale(ngtv)  0.182  0.732       
## numID       -0.788  0.056  0.038
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0114173 (tol = 0.002, component 1)
m<-lmer(positive ~  outdegree + indegree + numID + ( outdegree + indegree | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: positive ~ outdegree + indegree + numID + (outdegree + indegree |  
##     subID)
##    Data: fullData
## 
## REML criterion at convergence: 18148.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0855 -0.4876  0.2808  0.6541  2.0780 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  subID    (Intercept) 254.519  15.954              
##           outdegree     3.762   1.940   -0.44      
##           indegree      2.425   1.557   -0.61  0.04
##  Residual             809.399  28.450              
## Number of obs: 1879, groups:  subID, 210
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  71.6835     2.1169 304.3606  33.863   <2e-16 ***
## outdegree    -0.1219     0.4417  62.4226  -0.276   0.7834    
## indegree      1.1853     0.4287  27.0244   2.765   0.0101 *  
## numID        -0.1147     0.1329 177.1610  -0.863   0.3891    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr) outdgr indegr
## outdegree -0.166              
## indegree  -0.145 -0.104       
## numID     -0.654 -0.208 -0.265
m<-lmer(negative ~  outdegree + indegree + numID + ( outdegree + indegree | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: negative ~ outdegree + indegree + numID + (outdegree + indegree |  
##     subID)
##    Data: fullData
## 
## REML criterion at convergence: 15342.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.8807 -0.7812 -0.1741  0.7629  2.5569 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  subID    (Intercept)  308.115 17.553              
##           outdegree      1.812  1.346   -0.20      
##           indegree       6.301  2.510   -0.63  0.41
##  Residual             1032.610 32.134              
## Number of obs: 1548, groups:  subID, 207
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  40.5571     2.5658 265.9755  15.807   <2e-16 ***
## outdegree     0.2995     0.4915  36.2620   0.609   0.5461    
## indegree     -1.0552     0.5875  37.7387  -1.796   0.0805 .  
## numID         0.1652     0.1586 163.0605   1.042   0.2992    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr) outdgr indegr
## outdegree -0.182              
## indegree  -0.202  0.008       
## numID     -0.664 -0.163 -0.238

PANAS

Outdegree

More positive and more negative, experience causes more experiences

m<-glmer(outdegree ~  scale(PANAS_P) + scale(PANAS_N) + numID + ( scale(PANAS_P) + scale(PANAS_N) | subID), data=fullData,family="poisson")
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0420052 (tol = 0.002, component 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: 
## outdegree ~ scale(PANAS_P) + scale(PANAS_N) + numID + (scale(PANAS_P) +  
##     scale(PANAS_N) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7289.2   7345.3  -3634.6   7269.2     2014 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2547 -0.9050 -0.1423  0.5655  8.0179 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr       
##  subID  (Intercept)    0.21270  0.4612              
##         scale(PANAS_P) 0.08237  0.2870   -0.14      
##         scale(PANAS_N) 0.05482  0.2341   -0.37  0.82
## Number of obs: 2024, groups:  subID, 210
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.270975   0.073638  -3.680 0.000233 ***
## scale(PANAS_P)  0.130171   0.040121   3.244 0.001177 ** 
## scale(PANAS_N)  0.203885   0.036301   5.617 1.95e-08 ***
## numID           0.041642   0.004371   9.526  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANAS_P s(PANAS_N
## sc(PANAS_P) -0.096                    
## sc(PANAS_N) -0.121  0.733             
## numID       -0.807  0.033    -0.025   
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0420052 (tol = 0.002, component 1)
m<-glmer(outdegree ~  scale(PANAS_1) + numID + ( scale(PANAS_1) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: outdegree ~ scale(PANAS_1) + numID + (scale(PANAS_1) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7382.6   7416.3  -3685.3   7370.6     2020 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.1696 -0.9468 -0.1457  0.6021  8.5283 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr
##  subID  (Intercept)    0.2123   0.4607       
##         scale(PANAS_1) 0.0365   0.1910   0.09
## Number of obs: 2026, groups:  subID, 211
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.219332   0.072973  -3.006  0.00265 ** 
## scale(PANAS_1) -0.005384   0.028583  -0.188  0.85059    
## numID           0.038786   0.004372   8.871  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_1) -0.006       
## numID       -0.811  0.029
m<-glmer(outdegree ~  scale(PANAS_2) + numID + ( scale(PANAS_2) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: outdegree ~ scale(PANAS_2) + numID + (scale(PANAS_2) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7360.3   7394.0  -3674.2   7348.3     2014 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2057 -0.9420 -0.1321  0.5946  8.5392 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr
##  subID  (Intercept)    0.21560  0.4643       
##         scale(PANAS_2) 0.03714  0.1927   0.11
## Number of obs: 2020, groups:  subID, 211
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.223538   0.073380  -3.046  0.00232 ** 
## scale(PANAS_2) -0.012244   0.028681  -0.427  0.66946    
## numID           0.038849   0.004397   8.835  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_2)  0.002       
## numID       -0.811  0.034
m<-glmer(outdegree ~  scale(PANAS_3) + numID + ( scale(PANAS_3) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: outdegree ~ scale(PANAS_3) + numID + (scale(PANAS_3) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7388.5   7422.1  -3688.2   7376.5     2018 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2179 -0.9614 -0.1375  0.6147  8.7183 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr
##  subID  (Intercept)    0.2116   0.460        
##         scale(PANAS_3) 0.0279   0.167    0.04
## Number of obs: 2024, groups:  subID, 211
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.215610   0.072760  -2.963  0.00304 ** 
## scale(PANAS_3) -0.007147   0.026949  -0.265  0.79086    
## numID           0.038851   0.004364   8.903  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_3) -0.011       
## numID       -0.812  0.022
m<-glmer(outdegree ~  scale(PANAS_4) + numID + ( scale(PANAS_4) | subID), data=fullData,family="poisson")
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00277919 (tol = 0.002, component 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: outdegree ~ scale(PANAS_4) + numID + (scale(PANAS_4) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7355.5   7389.2  -3671.8   7343.5     2010 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.1589 -0.9462 -0.1283  0.6123  8.6525 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr
##  subID  (Intercept)    0.2165   0.4653       
##         scale(PANAS_4) 0.0315   0.1775   0.05
## Number of obs: 2016, groups:  subID, 211
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.210564   0.073286  -2.873  0.00406 ** 
## scale(PANAS_4) -0.010645   0.027558  -0.386  0.69929    
## numID           0.038464   0.004405   8.733  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_4)  0.000       
## numID       -0.811  0.019
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00277919 (tol = 0.002, component 1)
m<-glmer(outdegree ~  scale(PANAS_5) + numID + ( scale(PANAS_5) | subID), data=fullData,family="poisson")
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00225143 (tol = 0.002, component 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: outdegree ~ scale(PANAS_5) + numID + (scale(PANAS_5) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7326.7   7360.3  -3657.4   7314.7     2007 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2607 -0.9321 -0.1393  0.6102  8.3581 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr
##  subID  (Intercept)    0.20451  0.4522       
##         scale(PANAS_5) 0.05001  0.2236   0.02
## Number of obs: 2013, groups:  subID, 210
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.222421   0.072794  -3.055  0.00225 ** 
## scale(PANAS_5)  0.012754   0.030668   0.416  0.67749    
## numID           0.038413   0.004343   8.844  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_5) -0.035       
## numID       -0.811  0.037
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00225143 (tol = 0.002, component 1)
m<-glmer(outdegree ~  scale(PANAS_6) + numID + ( scale(PANAS_6) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: outdegree ~ scale(PANAS_6) + numID + (scale(PANAS_6) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7289.7   7323.3  -3638.8   7277.7     1998 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.1622 -0.9458 -0.1438  0.5814 10.2155 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr 
##  subID  (Intercept)    0.22693  0.4764        
##         scale(PANAS_6) 0.02762  0.1662   -0.27
## Number of obs: 2004, groups:  subID, 211
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.208671   0.073557  -2.837  0.00456 ** 
## scale(PANAS_6)  0.067077   0.026218   2.558  0.01051 *  
## numID           0.038362   0.004439   8.642  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_6) -0.056       
## numID       -0.807 -0.032
m<-glmer(outdegree ~  scale(PANAS_7) + numID + ( scale(PANAS_7) | subID), data=fullData,family="poisson")
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00227512 (tol = 0.002, component 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: outdegree ~ scale(PANAS_7) + numID + (scale(PANAS_7) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7332.8   7366.5  -3660.4   7320.8     2009 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.1972 -0.9538 -0.1640  0.6094  9.7612 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr 
##  subID  (Intercept)    0.22509  0.4744        
##         scale(PANAS_7) 0.02393  0.1547   -0.32
## Number of obs: 2015, groups:  subID, 211
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.223109   0.073441  -3.038 0.002382 ** 
## scale(PANAS_7)  0.085580   0.025708   3.329 0.000872 ***
## numID           0.039254   0.004401   8.919  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_7) -0.074       
## numID       -0.806 -0.032
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00227512 (tol = 0.002, component 1)
m<-glmer(outdegree ~  scale(PANAS_8) + numID + ( scale(PANAS_8) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: outdegree ~ scale(PANAS_8) + numID + (scale(PANAS_8) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7345.6   7379.2  -3666.8   7333.6     2011 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2594 -0.9539 -0.1602  0.5810  9.9169 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr 
##  subID  (Intercept)    0.22548  0.4748        
##         scale(PANAS_8) 0.02235  0.1495   -0.42
## Number of obs: 2017, groups:  subID, 211
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.227962   0.072882  -3.128  0.00176 ** 
## scale(PANAS_8)  0.107117   0.024562   4.361 1.29e-05 ***
## numID           0.040131   0.004345   9.237  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_8) -0.088       
## numID       -0.804 -0.066
m<-glmer(outdegree ~  scale(PANAS_9) + numID + ( scale(PANAS_9) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: outdegree ~ scale(PANAS_9) + numID + (scale(PANAS_9) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7321.1   7354.7  -3654.5   7309.1     2012 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2537 -0.9417 -0.1545  0.5878  9.8810 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr 
##  subID  (Intercept)    0.22060  0.4697        
##         scale(PANAS_9) 0.02037  0.1427   -0.35
## Number of obs: 2018, groups:  subID, 211
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.217340   0.072759  -2.987 0.002816 ** 
## scale(PANAS_9)  0.089319   0.024566   3.636 0.000277 ***
## numID           0.038945   0.004356   8.940  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_9) -0.071       
## numID       -0.807 -0.048
m<-glmer(outdegree ~  scale(PANAS_10) + numID + ( scale(PANAS_10) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: outdegree ~ scale(PANAS_10) + numID + (scale(PANAS_10) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7333.4   7367.0  -3660.7   7321.4     2009 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.0771 -0.9399 -0.1344  0.6036 10.4137 
## 
## Random effects:
##  Groups Name            Variance Std.Dev. Corr 
##  subID  (Intercept)     0.21964  0.4687        
##         scale(PANAS_10) 0.02394  0.1547   -0.42
## Number of obs: 2015, groups:  subID, 211
## 
## Fixed effects:
##                  Estimate Std. Error z value Pr(>|z|)    
## (Intercept)     -0.222271   0.072005  -3.087  0.00202 ** 
## scale(PANAS_10)  0.113663   0.025030   4.541 5.59e-06 ***
## numID            0.039908   0.004289   9.305  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## s(PANAS_10) -0.078       
## numID       -0.803 -0.074

Indegree

More positive, more experiences cause an experience

m<-glmer(indegree ~  scale(PANAS_P) + scale(PANAS_N) + numID + ( scale(PANAS_P) + scale(PANAS_N) | subID), data=fullData,family="poisson")
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0150649 (tol = 0.002, component 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: 
## indegree ~ scale(PANAS_P) + scale(PANAS_N) + numID + (scale(PANAS_P) +  
##     scale(PANAS_N) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   6999.3   7055.5  -3489.7   6979.3     2014 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7296 -0.6425 -0.1311  0.2854  8.9860 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr       
##  subID  (Intercept)    0.20783  0.4559              
##         scale(PANAS_P) 0.03141  0.1772   -0.15      
##         scale(PANAS_N) 0.03976  0.1994    0.02  0.63
## Number of obs: 2024, groups:  subID, 210
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.207496   0.073023  -2.842  0.00449 ** 
## scale(PANAS_P)  0.098100   0.034287   2.861  0.00422 ** 
## scale(PANAS_N)  0.021779   0.035710   0.610  0.54194    
## numID           0.038190   0.004422   8.637  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANAS_P s(PANAS_N
## sc(PANAS_P) -0.062                    
## sc(PANAS_N) -0.068  0.648             
## numID       -0.813  0.007     0.090   
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0150649 (tol = 0.002, component 1)
m<-glmer(indegree ~  scale(PANAS_1) + numID + ( scale(PANAS_1) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: indegree ~ scale(PANAS_1) + numID + (scale(PANAS_1) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7057.8   7091.5  -3522.9   7045.8     2020 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3746 -0.6618 -0.1311  0.2898 10.6200 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr 
##  subID  (Intercept)    0.21300  0.4615        
##         scale(PANAS_1) 0.02068  0.1438   -0.17
## Number of obs: 2026, groups:  subID, 211
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.193577   0.072274  -2.678   0.0074 ** 
## scale(PANAS_1)  0.065117   0.025886   2.516   0.0119 *  
## numID           0.037734   0.004356   8.662   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_1) -0.018       
## numID       -0.811 -0.059
m<-glmer(indegree ~  scale(PANAS_2) + numID + ( scale(PANAS_2) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: indegree ~ scale(PANAS_2) + numID + (scale(PANAS_2) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7017.8   7051.5  -3502.9   7005.8     2014 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3750 -0.6805 -0.1314  0.2929  9.5407 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr 
##  subID  (Intercept)    0.20989  0.4581        
##         scale(PANAS_2) 0.03127  0.1768   -0.20
## Number of obs: 2020, groups:  subID, 211
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.193787   0.072165  -2.685  0.00725 ** 
## scale(PANAS_2)  0.069461   0.027994   2.481  0.01309 *  
## numID           0.037141   0.004341   8.556  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_2) -0.018       
## numID       -0.809 -0.081
m<-glmer(indegree ~  scale(PANAS_3) + numID + ( scale(PANAS_3) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: indegree ~ scale(PANAS_3) + numID + (scale(PANAS_3) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7020.7   7054.4  -3504.3   7008.7     2018 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3803 -0.6669 -0.1259  0.2769 10.5421 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr 
##  subID  (Intercept)    0.21454  0.4632        
##         scale(PANAS_3) 0.02466  0.1570   -0.17
## Number of obs: 2024, groups:  subID, 211
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.199603   0.072439  -2.755 0.005861 ** 
## scale(PANAS_3)  0.090778   0.026861   3.379 0.000726 ***
## numID           0.037541   0.004365   8.601  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_3) -0.032       
## numID       -0.809 -0.056
m<-glmer(indegree ~  scale(PANAS_4) + numID + ( scale(PANAS_4) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: indegree ~ scale(PANAS_4) + numID + (scale(PANAS_4) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7002.3   7036.0  -3495.2   6990.3     2010 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3712 -0.7003 -0.1421  0.2903 10.5508 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr 
##  subID  (Intercept)    0.21145  0.4598        
##         scale(PANAS_4) 0.02809  0.1676   -0.27
## Number of obs: 2016, groups:  subID, 211
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.196896   0.072056  -2.733  0.00628 ** 
## scale(PANAS_4)  0.083715   0.027408   3.054  0.00225 ** 
## numID           0.037457   0.004321   8.669  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_4) -0.022       
## numID       -0.808 -0.097
m<-glmer(indegree ~  scale(PANAS_5) + numID + ( scale(PANAS_5) | subID), data=fullData,family="poisson")
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00524094 (tol = 0.002, component 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: indegree ~ scale(PANAS_5) + numID + (scale(PANAS_5) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   6983.8   7017.5  -3485.9   6971.8     2007 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3954 -0.6967 -0.1409  0.2874  9.6745 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr 
##  subID  (Intercept)    0.21323  0.4618        
##         scale(PANAS_5) 0.02621  0.1619   -0.16
## Number of obs: 2013, groups:  subID, 210
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.212147   0.072628  -2.921 0.003489 ** 
## scale(PANAS_5)  0.103939   0.026837   3.873 0.000108 ***
## numID           0.038484   0.004355   8.837  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_5) -0.065       
## numID       -0.809 -0.013
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00524094 (tol = 0.002, component 1)
m<-glmer(indegree ~  scale(PANAS_6) + numID + ( scale(PANAS_6) | subID), data=fullData,family="poisson")
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00247089 (tol = 0.002, component 1)
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: indegree ~ scale(PANAS_6) + numID + (scale(PANAS_6) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   6962.3   6995.9  -3475.1   6950.3     1998 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4588 -0.6833 -0.1260  0.3078  9.0008 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr
##  subID  (Intercept)    0.2118   0.4602       
##         scale(PANAS_6) 0.0233   0.1526   0.11
## Number of obs: 2004, groups:  subID, 211
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.194635   0.072596  -2.681  0.00734 ** 
## scale(PANAS_6) -0.051159   0.026512  -1.930  0.05365 .  
## numID           0.037377   0.004391   8.512  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_6) -0.020       
## numID       -0.812  0.085
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00247089 (tol = 0.002, component 1)
m<-glmer(indegree ~  scale(PANAS_7) + numID + ( scale(PANAS_7) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: indegree ~ scale(PANAS_7) + numID + (scale(PANAS_7) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7024.0   7057.7  -3506.0   7012.0     2009 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2003 -0.6976 -0.1192  0.2948  8.8923 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr
##  subID  (Intercept)    0.22212  0.4713       
##         scale(PANAS_7) 0.02101  0.1450   0.06
## Number of obs: 2015, groups:  subID, 211
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.193717   0.073828  -2.624  0.00869 ** 
## scale(PANAS_7) -0.017714   0.026404  -0.671  0.50229    
## numID           0.037200   0.004477   8.309  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_7) -0.043       
## numID       -0.812  0.084
m<-glmer(indegree ~  scale(PANAS_8) + numID + ( scale(PANAS_8) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: indegree ~ scale(PANAS_8) + numID + (scale(PANAS_8) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7025.2   7058.8  -3506.6   7013.2     2011 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2262 -0.6747 -0.1146  0.3114  9.3700 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr
##  subID  (Intercept)    0.2174   0.4663       
##         scale(PANAS_8) 0.0218   0.1477   0.16
## Number of obs: 2017, groups:  subID, 211
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.188103   0.073252  -2.568   0.0102 *  
## scale(PANAS_8) -0.028800   0.026068  -1.105   0.2693    
## numID           0.036948   0.004441   8.320   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_8) -0.048       
## numID       -0.813  0.118
m<-glmer(indegree ~  scale(PANAS_9) + numID + ( scale(PANAS_9) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: indegree ~ scale(PANAS_9) + numID + (scale(PANAS_9) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7035.7   7069.4  -3511.9   7023.7     2012 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2298 -0.6648 -0.1104  0.2917  9.3696 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr
##  subID  (Intercept)    0.2173   0.4662       
##         scale(PANAS_9) 0.0234   0.1530   0.12
## Number of obs: 2018, groups:  subID, 211
## 
## Fixed effects:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.19218    0.07328  -2.623  0.00873 ** 
## scale(PANAS_9) -0.02539    0.02613  -0.972  0.33127    
## numID           0.03747    0.00444   8.439  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## sc(PANAS_9) -0.035       
## numID       -0.813  0.092
m<-glmer(indegree ~  scale(PANAS_10) + numID + ( scale(PANAS_10) | subID), data=fullData,family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: indegree ~ scale(PANAS_10) + numID + (scale(PANAS_10) | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##   7009.5   7043.2  -3498.8   6997.5     2009 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4484 -0.6821 -0.1305  0.2984 10.0272 
## 
## Random effects:
##  Groups Name            Variance Std.Dev. Corr
##  subID  (Intercept)     0.21470  0.4634       
##         scale(PANAS_10) 0.01904  0.1380   0.09
## Number of obs: 2015, groups:  subID, 211
## 
## Fixed effects:
##                  Estimate Std. Error z value Pr(>|z|)    
## (Intercept)     -0.190824   0.073199  -2.607  0.00914 ** 
## scale(PANAS_10) -0.024909   0.025675  -0.970  0.33197    
## numID            0.037077   0.004458   8.317  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PANA
## s(PANAS_10) -0.052       
## numID       -0.814  0.110

H2: People will be more certain in memories with more downstream dependents.

Causing more experiences and being caused by more experiences is associated with greater certainty in experience, but causing is a stronger effect.

Using strength/similarity is stronger effect.

m<-lmer(scale(Cert) ~  scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## scale(Cert) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +  
##     (scale(outdegree) + scale(indegree) | subID)
##    Data: fullData
## 
## REML criterion at convergence: 5031.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.6090 -0.5059  0.1124  0.5878  2.8176 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr       
##  subID    (Intercept)      0.307234 0.55429             
##           scale(outdegree) 0.057874 0.24057  -0.25      
##           scale(indegree)  0.006803 0.08248  -0.30 -0.19
##  Residual                  0.597374 0.77290             
## Number of obs: 1978, groups:  subID, 209
## 
## Fixed effects:
##                    Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)       1.477e-01  7.699e-02  2.521e+02   1.918  0.05623 .  
## scale(outdegree)  9.801e-02  3.474e-02  8.611e+01   2.821  0.00594 ** 
## scale(indegree)   5.612e-02  2.643e-02  1.985e+01   2.124  0.04648 *  
## numID            -7.075e-03  5.004e-03  1.778e+02  -1.414  0.15918    
## scale(length)    -1.175e-01  2.153e-02  1.949e+03  -5.457 5.45e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(t) scl(n) numID 
## scale(tdgr)  0.193                     
## scale(ndgr)  0.139 -0.114              
## numID       -0.807 -0.229 -0.180       
## scal(lngth) -0.081 -0.074  0.118  0.049
m<-lmer(scale(Cert) ~  scale(strengthIn) + scale(strengthOut) + ( scale(strengthIn) + scale(strengthOut) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## scale(Cert) ~ scale(strengthIn) + scale(strengthOut) + (scale(strengthIn) +  
##     scale(strengthOut) | subID)
##    Data: fullData
## 
## REML criterion at convergence: 5182.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.7369 -0.5148  0.1246  0.5897  2.6249 
## 
## Random effects:
##  Groups   Name               Variance Std.Dev. Corr       
##  subID    (Intercept)        0.29490  0.5430              
##           scale(strengthIn)  0.01240  0.1113   -0.23      
##           scale(strengthOut) 0.02925  0.1710   -0.43 -0.26
##  Residual                    0.60773  0.7796              
## Number of obs: 2044, groups:  subID, 211
## 
## Fixed effects:
##                     Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)          0.05506    0.04389 206.39256   1.255 0.211075    
## scale(strengthIn)    0.09384    0.02863  24.99336   3.278 0.003068 ** 
## scale(strengthOut)   0.11503    0.02895  60.50874   3.974 0.000191 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(I)
## scl(strngI)  0.025       
## scl(strngO) -0.085 -0.275

H3: Memories with more dependents will be more clearly defined and accessible.

Experiences causing more experiences are more predictive of clarity than experiences caused by more experiences

m<-lmer( scale(Clear) ~  scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## scale(Clear) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +  
##     (scale(outdegree) + scale(indegree) | subID)
##    Data: fullData
## 
## REML criterion at convergence: 5140.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.2286 -0.4757  0.1554  0.6171  3.2107 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr       
##  subID    (Intercept)      0.289103 0.53768             
##           scale(outdegree) 0.034093 0.18464  -0.16      
##           scale(indegree)  0.005779 0.07602  -0.16 -0.57
##  Residual                  0.630087 0.79378             
## Number of obs: 1999, groups:  subID, 209
## 
## Fixed effects:
##                    Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)       1.890e-01  7.424e-02  2.564e+02   2.546   0.0115 *  
## scale(outdegree)  7.679e-02  3.105e-02  8.094e+01   2.473   0.0155 *  
## scale(indegree)   3.706e-02  2.571e-02  1.740e+01   1.441   0.1673    
## numID            -9.836e-03  4.836e-03  1.801e+02  -2.034   0.0434 *  
## scale(length)    -1.188e-01  2.180e-02  1.974e+03  -5.449 5.69e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(t) scl(n) numID 
## scale(tdgr)  0.176                     
## scale(ndgr)  0.118 -0.221              
## numID       -0.801 -0.182 -0.142       
## scal(lngth) -0.081 -0.077  0.129  0.049
m<-lmer( scale(Clear) ~  scale(strengthIn) + scale(strengthOut) + numID + scale(length) + ( scale(strengthIn) + scale(strengthOut) | subID), data=fullData)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00223588 (tol = 0.002, component 1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Clear) ~ scale(strengthIn) + scale(strengthOut) + numID +  
##     scale(length) + (scale(strengthIn) + scale(strengthOut) |      subID)
##    Data: fullData
## 
## REML criterion at convergence: 5131.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.3406 -0.4673  0.1358  0.6127  3.2332 
## 
## Random effects:
##  Groups   Name               Variance Std.Dev. Corr       
##  subID    (Intercept)        0.286449 0.53521             
##           scale(strengthIn)  0.008327 0.09125  -0.22      
##           scale(strengthOut) 0.018026 0.13426  -0.38 -0.30
##  Residual                    0.632690 0.79542             
## Number of obs: 1999, groups:  subID, 209
## 
## Fixed effects:
##                      Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)         2.191e-01  7.461e-02  2.635e+02   2.936  0.00362 ** 
## scale(strengthIn)   5.984e-02  2.784e-02  2.935e+01   2.150  0.03996 *  
## scale(strengthOut)  1.200e-01  2.802e-02  6.655e+01   4.282 6.08e-05 ***
## numID              -1.143e-02  4.831e-03  1.891e+02  -2.367  0.01897 *  
## scale(length)      -1.176e-01  2.167e-02  1.958e+03  -5.428 6.40e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(I) scl(O) numID 
## scl(strngI)  0.140                     
## scl(strngO)  0.172 -0.229              
## numID       -0.807 -0.163 -0.244       
## scal(lngth) -0.074  0.135 -0.065  0.043
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00223588 (tol = 0.002, component 1)

H5: Memories with more dependents will be more fundamental to how people see themselves, and if they were changed, would change the person.

The number of an experiences of causes, but not what it is caused by, predict how fundamental an experience is.

m<-lmer( scale(Fund) ~  scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## scale(Fund) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +  
##     (scale(outdegree) + scale(indegree) | subID)
##    Data: fullData
## 
## REML criterion at convergence: 5206.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2467 -0.5500  0.1269  0.6305  2.7223 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr       
##  subID    (Intercept)      0.23053  0.48013             
##           scale(outdegree) 0.01731  0.13157  -0.62      
##           scale(indegree)  0.00201  0.04484   0.11  0.34
##  Residual                  0.67263  0.82014             
## Number of obs: 2001, groups:  subID, 209
## 
## Fixed effects:
##                    Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)       3.030e-01  7.013e-02  2.787e+02   4.321 2.17e-05 ***
## scale(outdegree)  2.411e-01  2.681e-02  6.215e+01   8.990 7.56e-13 ***
## scale(indegree)   5.018e-02  2.397e-02  6.440e+00   2.094 0.077981 .  
## numID            -1.653e-02  4.445e-03  1.902e+02  -3.719 0.000263 ***
## scale(length)     4.230e-02  2.193e-02  1.854e+03   1.929 0.053890 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(t) scl(n) numID 
## scale(tdgr)  0.213                     
## scale(ndgr)  0.112 -0.019              
## numID       -0.811 -0.345 -0.077       
## scal(lngth) -0.100 -0.089  0.130  0.070
m<-lmer( scale(Fund) ~  scale(strengthIn) + scale(strengthOut) + numID + scale(length) + ( scale(strengthIn) + scale(strengthOut) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Fund) ~ scale(strengthIn) + scale(strengthOut) + numID +  
##     scale(length) + (scale(strengthIn) + scale(strengthOut) |      subID)
##    Data: fullData
## 
## REML criterion at convergence: 5172.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3264 -0.5650  0.1118  0.6422  2.8645 
## 
## Random effects:
##  Groups   Name               Variance Std.Dev. Corr       
##  subID    (Intercept)        0.231291 0.48093             
##           scale(strengthIn)  0.006982 0.08356   0.12      
##           scale(strengthOut) 0.013599 0.11661  -0.67  0.66
##  Residual                    0.659268 0.81195             
## Number of obs: 2001, groups:  subID, 209
## 
## Fixed effects:
##                      Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)         3.393e-01  7.025e-02  2.909e+02   4.830 2.21e-06 ***
## scale(strengthIn)   7.023e-02  2.714e-02  4.316e+01   2.588   0.0131 *  
## scale(strengthOut)  2.688e-01  2.596e-02  9.901e+01  10.357  < 2e-16 ***
## numID              -1.866e-02  4.431e-03  1.980e+02  -4.211 3.85e-05 ***
## scale(length)       5.370e-02  2.172e-02  1.945e+03   2.473   0.0135 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(I) scl(O) numID 
## scl(strngI)  0.130                     
## scl(strngO)  0.247  0.070              
## numID       -0.808 -0.049 -0.379       
## scal(lngth) -0.087  0.131 -0.066  0.059
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

Important principal factor (Changed me, Fundamental, Representative)

m<-lmer(scale(PCAimp) ~  scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(PCAimp) ~ scale(outdegree) + scale(indegree) + numID +  
##     scale(length) + (scale(outdegree) + scale(indegree) | subID)
##    Data: fullData
## 
## REML criterion at convergence: 5053.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3852 -0.5216  0.0902  0.6224  2.7075 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr       
##  subID    (Intercept)      0.268839 0.51850             
##           scale(outdegree) 0.016052 0.12670  -0.60      
##           scale(indegree)  0.007867 0.08869   0.03  0.07
##  Residual                  0.608755 0.78023             
## Number of obs: 2001, groups:  subID, 209
## 
## Fixed effects:
##                    Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)       3.677e-01  7.242e-02  2.756e+02   5.078 7.03e-07 ***
## scale(outdegree)  2.304e-01  2.594e-02  7.478e+01   8.879 2.58e-13 ***
## scale(indegree)   6.813e-02  2.726e-02  2.695e+01   2.499   0.0188 *  
## numID            -1.982e-02  4.664e-03  1.978e+02  -4.249 3.31e-05 ***
## scale(length)     3.321e-02  2.115e-02  1.903e+03   1.570   0.1165    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(t) scl(n) numID 
## scale(tdgr)  0.199                     
## scale(ndgr)  0.130 -0.051              
## numID       -0.805 -0.332 -0.091       
## scal(lngth) -0.091 -0.086  0.118  0.063
m<-lmer(scale(PCAimp) ~  scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(PCAimp) ~ scale(strengthOut) + scale(strengthIn) + numID +  
##     scale(length) + (scale(strengthOut) + scale(strengthIn) |      subID)
##    Data: fullData
## 
## REML criterion at convergence: 5011.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4462 -0.5297  0.0793  0.6167  2.7160 
## 
## Random effects:
##  Groups   Name               Variance Std.Dev. Corr       
##  subID    (Intercept)        0.27011  0.5197              
##           scale(strengthOut) 0.01080  0.1039   -0.73      
##           scale(strengthIn)  0.01076  0.1037    0.24  0.49
##  Residual                    0.59734  0.7729              
## Number of obs: 2001, groups:  subID, 209
## 
## Fixed effects:
##                      Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)         4.079e-01  7.178e-02  2.872e+02   5.682 3.27e-08 ***
## scale(strengthOut)  2.607e-01  2.420e-02  1.282e+02  10.773  < 2e-16 ***
## scale(strengthIn)   9.057e-02  2.829e-02  4.022e+01   3.201  0.00267 ** 
## numID              -2.222e-02  4.596e-03  2.049e+02  -4.833 2.63e-06 ***
## scale(length)       4.612e-02  2.086e-02  1.960e+03   2.211  0.02717 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(O) scl(I) numID 
## scl(strngO)  0.247                     
## scl(strngI)  0.121  0.065              
## numID       -0.798 -0.399  0.000       
## scal(lngth) -0.081 -0.064  0.130  0.056
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

H6: Memories with more dependents will be more important to the person.

To Self

Experiences that cause more experiences are perceived as important to self, but not experiences that are caused by more experiences.

m<-lmer( scale(IM) ~  scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## scale(IM) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +  
##     (scale(outdegree) + scale(indegree) | subID)
##    Data: fullData
## 
## REML criterion at convergence: 5312.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4772 -0.4132  0.2034  0.5973  2.5891 
## 
## Random effects:
##  Groups   Name             Variance  Std.Dev. Corr       
##  subID    (Intercept)      0.1893444 0.43514             
##           scale(outdegree) 0.0246494 0.15700  -0.37      
##           scale(indegree)  0.0004888 0.02211  -0.98  0.15
##  Residual                  0.7178590 0.84727             
## Number of obs: 2002, groups:  subID, 209
## 
## Fixed effects:
##                    Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)       3.489e-01  6.778e-02  3.037e+02   5.148 4.75e-07 ***
## scale(outdegree)  1.583e-01  2.951e-02  9.646e+01   5.363 5.60e-07 ***
## scale(indegree)   7.053e-02  2.183e-02  1.325e+03   3.231  0.00126 ** 
## numID            -1.917e-02  4.238e-03  1.972e+02  -4.523 1.05e-05 ***
## scale(length)    -6.436e-02  2.254e-02  1.922e+03  -2.855  0.00434 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(t) scl(n) numID 
## scale(tdgr)  0.205                     
## scale(ndgr)  0.143 -0.093              
## numID       -0.820 -0.264 -0.205       
## scal(lngth) -0.102 -0.085  0.132  0.068
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m<-lmer( scale(IM) ~  scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(IM) ~ scale(strengthOut) + scale(strengthIn) + numID +  
##     scale(length) + (scale(strengthOut) + scale(strengthIn) |      subID)
##    Data: fullData
## 
## REML criterion at convergence: 5272.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4657 -0.4128  0.1911  0.5905  2.5754 
## 
## Random effects:
##  Groups   Name               Variance Std.Dev. Corr       
##  subID    (Intercept)        0.182713 0.42745             
##           scale(strengthOut) 0.023005 0.15167  -0.55      
##           scale(strengthIn)  0.001829 0.04277  -0.16  0.91
##  Residual                    0.703560 0.83879             
## Number of obs: 2002, groups:  subID, 209
## 
## Fixed effects:
##                      Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)         3.889e-01  6.728e-02  3.229e+02   5.780 1.76e-08 ***
## scale(strengthOut)  1.966e-01  2.923e-02  9.618e+01   6.725 1.25e-09 ***
## scale(strengthIn)   7.710e-02  2.432e-02  7.275e+01   3.171  0.00223 ** 
## numID              -2.149e-02  4.156e-03  2.061e+02  -5.171 5.50e-07 ***
## scale(length)      -5.727e-02  2.223e-02  1.919e+03  -2.576  0.01006 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(O) scl(I) numID 
## scl(strngO)  0.238                     
## scl(strngI)  0.138  0.014              
## numID       -0.821 -0.341 -0.120       
## scal(lngth) -0.096 -0.069  0.135  0.064
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

To Others

Experiences that cause more experiences are perceived as important to others, but not experiences that are caused by more experiences.

m<-lmer( scale(IO) ~  scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## scale(IO) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +  
##     (scale(outdegree) + scale(indegree) | subID)
##    Data: fullData
## 
## REML criterion at convergence: 5386.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2061 -0.6042  0.1366  0.6781  2.3626 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr       
##  subID    (Intercept)      0.18664  0.4320              
##           scale(outdegree) 0.01514  0.1231   -0.48      
##           scale(indegree)  0.01821  0.1349   -0.39 -0.04
##  Residual                  0.74699  0.8643              
## Number of obs: 1999, groups:  subID, 209
## 
## Fixed effects:
##                    Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)       1.987e-01  6.882e-02  2.856e+02   2.887  0.00418 ** 
## scale(outdegree)  1.265e-01  2.767e-02  5.062e+01   4.572 3.14e-05 ***
## scale(indegree)   4.480e-02  3.230e-02  2.837e+01   1.387  0.17627    
## numID            -1.081e-02  4.271e-03  1.812e+02  -2.530  0.01226 *  
## scale(length)    -4.575e-02  2.294e-02  1.873e+03  -1.995  0.04622 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(t) scl(n) numID 
## scale(tdgr)  0.185                     
## scale(ndgr)  0.178 -0.135              
## numID       -0.826 -0.274 -0.235       
## scal(lngth) -0.099 -0.091  0.104  0.066
m<-lmer( scale(IO) ~  scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(IO) ~ scale(strengthOut) + scale(strengthIn) + numID +  
##     scale(length) + (scale(strengthOut) + scale(strengthIn) |      subID)
##    Data: fullData
## 
## REML criterion at convergence: 5374.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2080 -0.6087  0.1441  0.6728  2.5154 
## 
## Random effects:
##  Groups   Name               Variance Std.Dev. Corr       
##  subID    (Intercept)        0.18470  0.4298              
##           scale(strengthOut) 0.01045  0.1022   -0.56      
##           scale(strengthIn)  0.01748  0.1322   -0.32  0.32
##  Residual                    0.74478  0.8630              
## Number of obs: 1999, groups:  subID, 209
## 
## Fixed effects:
##                      Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)         2.324e-01  6.886e-02  2.932e+02   3.375 0.000838 ***
## scale(strengthOut)  1.422e-01  2.674e-02  4.664e+01   5.320 2.88e-06 ***
## scale(strengthIn)   7.125e-02  3.283e-02  3.189e+01   2.170 0.037542 *  
## numID              -1.272e-02  4.247e-03  1.863e+02  -2.995 0.003122 ** 
## scale(length)      -3.765e-02  2.283e-02  1.893e+03  -1.649 0.099224 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(O) scl(I) numID 
## scl(strngO)  0.205                     
## scl(strngI)  0.191 -0.074              
## numID       -0.825 -0.292 -0.211       
## scal(lngth) -0.088 -0.068  0.111  0.056

More Important to Self than Others

No evidence

fullData$ImpDiff <- (fullData$IM-fullData$IO)
m<-lmer( scale(ImpDiff) ~  scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(ImpDiff) ~ scale(strengthOut) + scale(strengthIn) + numID +  
##     scale(length) + (scale(strengthOut) + scale(strengthIn) |      subID)
##    Data: fullData
## 
## REML criterion at convergence: 5523
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3018 -0.5933 -0.2058  0.5344  3.5220 
## 
## Random effects:
##  Groups   Name               Variance Std.Dev. Corr       
##  subID    (Intercept)        0.15512  0.3939              
##           scale(strengthOut) 0.01130  0.1063   -0.19      
##           scale(strengthIn)  0.01862  0.1364   -0.13 -0.29
##  Residual                    0.81724  0.9040              
## Number of obs: 1999, groups:  subID, 209
## 
## Fixed effects:
##                      Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)         5.053e-02  6.595e-02  2.824e+02   0.766    0.444
## scale(strengthOut) -9.043e-03  2.850e-02  6.654e+01  -0.317    0.752
## scale(strengthIn)  -7.300e-03  3.445e-02  2.686e+01  -0.212    0.834
## numID              -3.020e-03  4.014e-03  1.675e+02  -0.752    0.453
## scale(length)      -2.838e-03  2.357e-02  1.831e+03  -0.120    0.904
## 
## Correlation of Fixed Effects:
##             (Intr) scl(O) scl(I) numID 
## scl(strngO)  0.170                     
## scl(strngI)  0.177 -0.231              
## numID       -0.821 -0.185 -0.165       
## scal(lngth) -0.092 -0.067  0.120  0.057
m<-lmer( scale(ImpDiff) ~  scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(ImpDiff) ~ scale(outdegree) + scale(indegree) + numID +  
##     scale(length) + (scale(outdegree) + scale(indegree) | subID)
##    Data: fullData
## 
## REML criterion at convergence: 5522
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2377 -0.5761 -0.2066  0.5357  3.5269 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr       
##  subID    (Intercept)      0.15463  0.3932              
##           scale(outdegree) 0.01526  0.1235   -0.19      
##           scale(indegree)  0.02248  0.1499   -0.21 -0.40
##  Residual                  0.81309  0.9017              
## Number of obs: 1999, groups:  subID, 209
## 
## Fixed effects:
##                    Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)       5.008e-02  6.599e-02  2.830e+02   0.759    0.449
## scale(outdegree) -1.517e-02  2.885e-02  6.510e+01  -0.526    0.601
## scale(indegree)   2.418e-03  3.472e-02  2.524e+01   0.070    0.945
## numID            -2.986e-03  4.031e-03  1.665e+02  -0.741    0.460
## scale(length)     7.946e-04  2.361e-02  1.825e+03   0.034    0.973
## 
## Correlation of Fixed Effects:
##             (Intr) scl(t) scl(n) numID 
## scale(tdgr)  0.169                     
## scale(ndgr)  0.171 -0.227              
## numID       -0.824 -0.190 -0.189       
## scal(lngth) -0.103 -0.088  0.107  0.067

H11: People think more often about memories with more memories causing them.

Experiences with more causes and caused by more are reflected on more frequently. Perhaps some stronger effects for causing more.

m<-lmer( scale(Often) ~  scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## scale(Often) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +  
##     (scale(outdegree) + scale(indegree) | subID)
##    Data: fullData
## 
## REML criterion at convergence: 5125.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5447 -0.6743 -0.0551  0.6695  2.7754 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr       
##  subID    (Intercept)      0.280069 0.52922             
##           scale(outdegree) 0.041688 0.20418   0.03      
##           scale(indegree)  0.002115 0.04599  -1.00 -0.06
##  Residual                  0.645235 0.80326             
## Number of obs: 1977, groups:  subID, 209
## 
## Fixed effects:
##                    Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)         0.43322    0.07428  264.34044   5.832 1.59e-08 ***
## scale(outdegree)    0.12297    0.03281   64.99111   3.748 0.000381 ***
## scale(indegree)     0.07828    0.02169  276.24537   3.608 0.000366 ***
## numID              -0.02422    0.00480  185.07745  -5.045 1.08e-06 ***
## scale(length)      -0.10483    0.02210 1959.37290  -4.744 2.25e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(t) scl(n) numID 
## scale(tdgr)  0.195                     
## scale(ndgr)  0.177 -0.074              
## numID       -0.802 -0.126 -0.307       
## scal(lngth) -0.075 -0.074  0.132  0.038
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m<-lmer( scale(Often) ~  scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
## Warning: Model failed to converge with 1 negative eigenvalue: -6.5e+02
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Often) ~ scale(strengthOut) + scale(strengthIn) + numID +  
##     scale(length) + (scale(strengthOut) + scale(strengthIn) |      subID)
##    Data: fullData
## 
## REML criterion at convergence: 5117.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5156 -0.6768 -0.0391  0.6767  2.8425 
## 
## Random effects:
##  Groups   Name               Variance  Std.Dev.  Corr       
##  subID    (Intercept)        2.769e-01 0.5262348            
##           scale(strengthOut) 1.763e-07 0.0004199 -1.00      
##           scale(strengthIn)  1.115e-02 0.1055889 -0.38  0.38
##  Residual                    6.574e-01 0.8108015            
## Number of obs: 1977, groups:  subID, 209
## 
## Fixed effects:
##                      Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)         4.661e-01  7.334e-02  2.608e+02   6.356 9.22e-10 ***
## scale(strengthOut)  1.587e-01  2.158e-02  1.295e+03   7.353 3.41e-13 ***
## scale(strengthIn)   1.162e-01  2.919e-02  2.284e+01   3.980 0.000597 ***
## numID              -2.585e-02  4.755e-03  1.869e+02  -5.437 1.68e-07 ***
## scale(length)      -9.569e-02  2.175e-02  1.957e+03  -4.400 1.14e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(O) scl(I) numID 
## scl(strngO)  0.129                     
## scl(strngI)  0.182 -0.141              
## numID       -0.802 -0.118 -0.230       
## scal(lngth) -0.061 -0.043  0.132  0.029
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

H12: The more memories that depend on a given memory, the more people believe “This memory changed me”. Weaker effect for memories with many causes of it.

m<-lmer( scale(Chan) ~  scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## scale(Chan) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +  
##     (scale(outdegree) + scale(indegree) | subID)
##    Data: fullData
## 
## REML criterion at convergence: 5216.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5976 -0.5322  0.1484  0.6224  2.7493 
## 
## Random effects:
##  Groups   Name             Variance  Std.Dev. Corr       
##  subID    (Intercept)      0.2310589 0.48069             
##           scale(outdegree) 0.0135199 0.11628  -0.62      
##           scale(indegree)  0.0001786 0.01336  -0.18  0.88
##  Residual                  0.6790026 0.82402             
## Number of obs: 2001, groups:  subID, 209
## 
## Fixed effects:
##                    Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)       3.569e-01  7.019e-02  2.751e+02   5.085 6.81e-07 ***
## scale(outdegree)  2.149e-01  2.586e-02  8.309e+01   8.309 1.57e-12 ***
## scale(indegree)   4.684e-02  2.174e-02  1.191e+02   2.154   0.0332 *  
## numID            -1.975e-02  4.464e-03  1.889e+02  -4.424 1.64e-05 ***
## scale(length)     3.090e-02  2.198e-02  1.889e+03   1.405   0.1601    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(t) scl(n) numID 
## scale(tdgr)  0.208                     
## scale(ndgr)  0.114 -0.039              
## numID       -0.812 -0.332 -0.113       
## scal(lngth) -0.098 -0.090  0.140  0.068
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m<-lmer( scale(Chan) ~  scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Chan) ~ scale(strengthOut) + scale(strengthIn) + numID +  
##     scale(length) + (scale(strengthOut) + scale(strengthIn) |      subID)
##    Data: fullData
## 
## REML criterion at convergence: 5179.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.7230 -0.5164  0.1368  0.6088  2.8039 
## 
## Random effects:
##  Groups   Name               Variance Std.Dev. Corr       
##  subID    (Intercept)        0.234197 0.48394             
##           scale(strengthOut) 0.011446 0.10698  -0.66      
##           scale(strengthIn)  0.003429 0.05855   0.32  0.50
##  Residual                    0.664514 0.81518             
## Number of obs: 2001, groups:  subID, 209
## 
## Fixed effects:
##                      Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)           0.38996    0.06988  282.23489   5.580 5.63e-08 ***
## scale(strengthOut)    0.24773    0.02551  103.26253   9.710 3.19e-16 ***
## scale(strengthIn)     0.05136    0.02525   45.71799   2.034   0.0478 *  
## numID                -0.02195    0.00443  192.91915  -4.955 1.57e-06 ***
## scale(length)         0.04049    0.02176 1933.14650   1.861   0.0629 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(O) scl(I) numID 
## scl(strngO)  0.233                     
## scl(strngI)  0.106 -0.012              
## numID       -0.805 -0.361 -0.019       
## scal(lngth) -0.087 -0.066  0.143  0.058
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

H13: The more memories that depend on a given memory, the more certain that people feel this experience is representative of who they are.

People feel experiences with more causes are more representative of self. Similar, but weaker, effect for experiences caused by more experiences.

m<-lmer( scale(Rep) ~  scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## scale(Rep) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +  
##     (scale(outdegree) + scale(indegree) | subID)
##    Data: fullData
## 
## REML criterion at convergence: 5090.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3859 -0.5487  0.0731  0.6374  2.9952 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr       
##  subID    (Intercept)      0.25619  0.5062              
##           scale(outdegree) 0.01300  0.1140   -0.36      
##           scale(indegree)  0.01362  0.1167   -0.14  0.00
##  Residual                  0.62194  0.7886              
## Number of obs: 2001, groups:  subID, 209
## 
## Fixed effects:
##                    Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)       3.366e-01  7.179e-02  2.812e+02   4.688 4.30e-06 ***
## scale(outdegree)  1.562e-01  2.577e-02  6.217e+01   6.062 8.66e-08 ***
## scale(indegree)   9.478e-02  2.992e-02  4.393e+01   3.168 0.002793 ** 
## numID            -1.786e-02  4.628e-03  1.982e+02  -3.860 0.000153 ***
## scale(length)     1.825e-02  2.139e-02  1.915e+03   0.853 0.393570    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(t) scl(n) numID 
## scale(tdgr)  0.183                     
## scale(ndgr)  0.160 -0.073              
## numID       -0.806 -0.239 -0.152       
## scal(lngth) -0.084 -0.083  0.113  0.054
m<-lmer( scale(Rep) ~  scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Rep) ~ scale(strengthOut) + scale(strengthIn) + numID +  
##     scale(length) + (scale(strengthOut) + scale(strengthIn) |      subID)
##    Data: fullData
## 
## REML criterion at convergence: 5065.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4235 -0.5514  0.0586  0.6461  2.9458 
## 
## Random effects:
##  Groups   Name               Variance Std.Dev. Corr       
##  subID    (Intercept)        0.253668 0.50365             
##           scale(strengthOut) 0.006451 0.08032  -0.66      
##           scale(strengthIn)  0.016450 0.12826   0.14  0.65
##  Residual                    0.617913 0.78607             
## Number of obs: 2001, groups:  subID, 209
## 
## Fixed effects:
##                      Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)         3.663e-01  7.107e-02  2.943e+02   5.154 4.68e-07 ***
## scale(strengthOut)  1.750e-01  2.302e-02  1.595e+02   7.602 2.36e-12 ***
## scale(strengthIn)   1.305e-01  3.091e-02  4.372e+01   4.220 0.000121 ***
## numID              -1.911e-02  4.537e-03  2.032e+02  -4.211 3.81e-05 ***
## scale(length)       2.974e-02  2.114e-02  1.966e+03   1.407 0.159606    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(O) scl(I) numID 
## scl(strngO)  0.237                     
## scl(strngI)  0.159  0.092              
## numID       -0.799 -0.343 -0.052       
## scal(lngth) -0.078 -0.059  0.121  0.051
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

Exploratory Analyses

Sentiment of memory description will be associated with dependencies

Combined

Experiences caused by more experiences have more qualitative positive sentiment.

m<-lmer( scale(vad_comp) ~  scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
## Warning: Model failed to converge with 1 negative eigenvalue: -5.9e+01
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_comp) ~ scale(outdegree) + scale(indegree) + numID +  
##     scale(length) + (scale(outdegree) + scale(indegree) | subID)
##    Data: fullData
## 
## REML criterion at convergence: 6252.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1215 -0.2884 -0.1403  0.2995  2.9679 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr     
##  subID    (Intercept)      0.052467 0.22906           
##           scale(outdegree) 0.001345 0.03667  1.00     
##           scale(indegree)  0.018997 0.13783  0.54 0.54
##  Residual                  0.928431 0.96355           
## Number of obs: 2215, groups:  subID, 215
## 
## Fixed effects:
##                    Estimate Std. Error         df t value Pr(>|t|)   
## (Intercept)       8.975e-02  5.256e-02  3.270e+02   1.708  0.08865 . 
## scale(outdegree)  4.230e-03  2.449e-02  1.747e+02   0.173  0.86306   
## scale(indegree)   9.365e-02  3.326e-02  3.969e+01   2.816  0.00754 **
## numID            -4.418e-03  2.748e-03  1.137e+02  -1.608  0.11063   
## scale(length)    -1.513e-02  2.271e-02  1.373e+03  -0.666  0.50522   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(t) scl(n) numID 
## scale(tdgr)  0.190                     
## scale(ndgr)  0.185 -0.050              
## numID       -0.828 -0.108 -0.020       
## scal(lngth) -0.139 -0.108  0.086  0.149
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m<-lmer( scale(vad_comp) ~  scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
## Warning: Model failed to converge with 1 negative eigenvalue: -3.9e+00
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_comp) ~ scale(strengthOut) + scale(strengthIn) + numID +  
##     scale(length) + (scale(strengthOut) + scale(strengthIn) |      subID)
##    Data: fullData
## 
## REML criterion at convergence: 6249.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3313 -0.2978 -0.1362  0.2809  2.9859 
## 
## Random effects:
##  Groups   Name               Variance  Std.Dev. Corr     
##  subID    (Intercept)        0.0580113 0.24086           
##           scale(strengthOut) 0.0009226 0.03037  1.00     
##           scale(strengthIn)  0.0201867 0.14208  0.53 0.53
##  Residual                    0.9241950 0.96135           
## Number of obs: 2215, groups:  subID, 215
## 
## Fixed effects:
##                      Estimate Std. Error         df t value Pr(>|t|)   
## (Intercept)         9.412e-02  5.336e-02  3.320e+02   1.764  0.07867 . 
## scale(strengthOut) -4.679e-03  2.474e-02  1.861e+02  -0.189  0.85021   
## scale(strengthIn)   1.010e-01  3.413e-02  4.409e+01   2.958  0.00496 **
## numID              -4.506e-03  2.822e-03  1.217e+02  -1.597  0.11292   
## scale(length)      -1.402e-02  2.269e-02  1.364e+03  -0.618  0.53667   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(O) scl(I) numID 
## scl(strngO)  0.182                     
## scl(strngI)  0.199 -0.098              
## numID       -0.825 -0.108 -0.031       
## scal(lngth) -0.123 -0.070  0.100  0.134
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

Positive

Experiences with more experiences causing them are qualitatively more positive

m<-lmer( scale(vad_pos) ~  scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
## Warning: Model failed to converge with 1 negative eigenvalue: -5.5e+00
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_pos) ~ scale(outdegree) + scale(indegree) + numID +  
##     scale(length) + (scale(outdegree) + scale(indegree) | subID)
##    Data: fullData
## 
## REML criterion at convergence: 6280.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.4990 -0.5707 -0.4513  0.2449  4.9708 
## 
## Random effects:
##  Groups   Name             Variance  Std.Dev. Corr       
##  subID    (Intercept)      0.0308695 0.17570             
##           scale(outdegree) 0.0002493 0.01579  -1.00      
##           scale(indegree)  0.0135756 0.11651   0.00  0.00
##  Residual                  0.9512739 0.97533             
## Number of obs: 2215, groups:  subID, 215
## 
## Fixed effects:
##                    Estimate Std. Error         df t value Pr(>|t|)   
## (Intercept)       5.235e-02  5.161e-02  4.055e+02   1.014  0.31100   
## scale(outdegree) -1.533e-02  2.340e-02  1.365e+03  -0.655  0.51236   
## scale(indegree)   8.794e-02  3.098e-02  3.174e+01   2.838  0.00784 **
## numID            -2.678e-03  2.682e-03  1.587e+02  -0.998  0.31959   
## scale(length)    -5.457e-02  2.271e-02  1.451e+03  -2.403  0.01639 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(t) scl(n) numID 
## scale(tdgr)  0.174                     
## scale(ndgr)  0.161 -0.192              
## numID       -0.863 -0.210 -0.121       
## scal(lngth) -0.153 -0.133  0.077  0.159
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m<-lmer( scale(vad_pos) ~  scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+01
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_pos) ~ scale(strengthOut) + scale(strengthIn) + numID +  
##     scale(length) + (scale(strengthOut) + scale(strengthIn) |      subID)
##    Data: fullData
## 
## REML criterion at convergence: 6280
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.4112 -0.5711 -0.4542  0.2452  4.9415 
## 
## Random effects:
##  Groups   Name               Variance Std.Dev. Corr       
##  subID    (Intercept)        0.031845 0.17845             
##           scale(strengthOut) 0.000428 0.02069  -1.00      
##           scale(strengthIn)  0.008238 0.09077  -0.05  0.05
##  Residual                    0.953023 0.97623             
## Number of obs: 2215, groups:  subID, 215
## 
## Fixed effects:
##                      Estimate Std. Error         df t value Pr(>|t|)   
## (Intercept)         5.653e-02  5.203e-02  4.084e+02   1.086   0.2780   
## scale(strengthOut) -1.947e-02  2.385e-02  1.698e+03  -0.816   0.4144   
## scale(strengthIn)   8.597e-02  2.921e-02  2.930e+01   2.943   0.0063 **
## numID              -3.003e-03  2.714e-03  1.658e+02  -1.107   0.2701   
## scale(length)      -5.471e-02  2.268e-02  1.439e+03  -2.412   0.0160 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(O) scl(I) numID 
## scl(strngO)  0.180                     
## scl(strngI)  0.164 -0.254              
## numID       -0.866 -0.221 -0.142       
## scal(lngth) -0.143 -0.103  0.085  0.148
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

Negative

No negative effects

m<-lmer( scale(vad_neg) ~  scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_neg) ~ scale(outdegree) + scale(indegree) + numID +  
##     scale(length) + (scale(outdegree) + scale(indegree) | subID)
##    Data: fullData
## 
## REML criterion at convergence: 6271.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2895 -0.4122 -0.2912 -0.1984  6.5233 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr       
##  subID    (Intercept)      0.064206 0.25339             
##           scale(outdegree) 0.005104 0.07144   0.28      
##           scale(indegree)  0.001228 0.03504  -0.97 -0.05
##  Residual                  0.932602 0.96571             
## Number of obs: 2215, groups:  subID, 215
## 
## Fixed effects:
##                    Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)      -6.519e-02  5.541e-02  3.214e+02  -1.176    0.240
## scale(outdegree) -2.064e-02  2.589e-02  8.614e+01  -0.797    0.427
## scale(indegree)  -3.201e-03  2.329e-02  1.446e+02  -0.137    0.891
## numID             3.234e-03  3.043e-03  1.447e+02   1.063    0.290
## scale(length)    -1.277e-03  2.331e-02  1.576e+03  -0.055    0.956
## 
## Correlation of Fixed Effects:
##             (Intr) scl(t) scl(n) numID 
## scale(tdgr)  0.172                     
## scale(ndgr)  0.146 -0.187              
## numID       -0.846 -0.120 -0.210       
## scal(lngth) -0.135 -0.107  0.099  0.131
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m<-lmer( scale(vad_neg) ~  scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_neg) ~ scale(strengthOut) + scale(strengthIn) + numID +  
##     scale(length) + (scale(strengthOut) + scale(strengthIn) |      subID)
##    Data: fullData
## 
## REML criterion at convergence: 6271.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2743 -0.4125 -0.2928 -0.1973  6.5147 
## 
## Random effects:
##  Groups   Name               Variance Std.Dev. Corr       
##  subID    (Intercept)        0.066791 0.25844             
##           scale(strengthOut) 0.003819 0.06179   0.72      
##           scale(strengthIn)  0.001439 0.03793  -0.86 -0.27
##  Residual                    0.933385 0.96612             
## Number of obs: 2215, groups:  subID, 215
## 
## Fixed effects:
##                      Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)        -6.008e-02  5.533e-02  3.313e+02  -1.086    0.278
## scale(strengthOut) -1.216e-02  2.601e-02  9.089e+01  -0.468    0.641
## scale(strengthIn)   1.618e-03  2.431e-02  8.086e+01   0.067    0.947
## numID               3.063e-03  3.034e-03  1.505e+02   1.010    0.314
## scale(length)      -2.765e-03  2.315e-02  1.542e+03  -0.119    0.905
## 
## Correlation of Fixed Effects:
##             (Intr) scl(O) scl(I) numID 
## scl(strngO)  0.147                     
## scl(strngI)  0.165 -0.243              
## numID       -0.841 -0.049 -0.223       
## scal(lngth) -0.121 -0.070  0.104  0.118
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

Number of words

No effect of causes or caused by on number of words

m<-glmer(nwords ~  outdegree + indegree + numID + scale(length) + ( outdegree + indegree | subID), data=fullData, family="poisson")
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: poisson  ( log )
## Formula: nwords ~ outdegree + indegree + numID + scale(length) + (outdegree +  
##     indegree | subID)
##    Data: fullData
## 
##      AIC      BIC   logLik deviance df.resid 
##  12504.6  12567.3  -6241.3  12482.6     2204 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.3076 -0.7071 -0.1331  0.5397 12.0926 
## 
## Random effects:
##  Groups Name        Variance Std.Dev. Corr       
##  subID  (Intercept) 0.568425 0.75394             
##         outdegree   0.002340 0.04838   0.12      
##         indegree    0.002723 0.05218  -0.38  0.26
## Number of obs: 2215, groups:  subID, 215
## 
## Fixed effects:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    1.9334800  0.0811461  23.827   <2e-16 ***
## outdegree     -0.0123978  0.0077872  -1.592    0.111    
## indegree      -0.0005592  0.0090943  -0.061    0.951    
## numID          0.0006189  0.0056419   0.110    0.913    
## scale(length)  0.0862643  0.0097761   8.824   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) outdgr indegr numID 
## outdegree   -0.099                     
## indegree    -0.081  0.116              
## numID       -0.738  0.044 -0.178       
## scal(lngth) -0.034 -0.047  0.085  0.014

Breadth

Experiences with more causes are more broad.

m<-lmer( scale(Breadth) ~  scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Breadth) ~ scale(outdegree) + scale(indegree) + numID +  
##     scale(length) + (scale(outdegree) + scale(indegree) | subID)
##    Data: fullData
## 
## REML criterion at convergence: 5255.5
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.38126 -0.64650 -0.00537  0.65203  2.99689 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr       
##  subID    (Intercept)      0.254937 0.50491             
##           scale(outdegree) 0.033977 0.18433  -0.08      
##           scale(indegree)  0.001751 0.04185  -0.01  0.94
##  Residual                  0.703320 0.83864             
## Number of obs: 1975, groups:  subID, 209
## 
## Fixed effects:
##                    Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)       2.435e-01  7.291e-02  2.532e+02   3.339 0.000967 ***
## scale(outdegree)  9.815e-02  3.180e-02  5.899e+01   3.086 0.003086 ** 
## scale(indegree)  -2.257e-02  2.359e-02  4.733e+00  -0.957 0.385073    
## numID            -1.430e-02  4.654e-03  1.716e+02  -3.072 0.002472 ** 
## scale(length)     4.908e-02  2.276e-02  1.916e+03   2.157 0.031163 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(t) scl(n) numID 
## scale(tdgr)  0.224                     
## scale(ndgr)  0.144  0.113              
## numID       -0.800 -0.187 -0.114       
## scal(lngth) -0.086 -0.075  0.134  0.051
m<-lmer( scale(Breadth) ~  scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
## Warning: Model failed to converge with 1 negative eigenvalue: -1.6e+03
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Breadth) ~ scale(strengthOut) + scale(strengthIn) + numID +  
##     scale(length) + (scale(strengthOut) + scale(strengthIn) |      subID)
##    Data: fullData
## 
## REML criterion at convergence: 5276
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3666 -0.6810 -0.0020  0.6681  3.1794 
## 
## Random effects:
##  Groups   Name               Variance  Std.Dev. Corr     
##  subID    (Intercept)        0.2663596 0.51610           
##           scale(strengthOut) 0.0004799 0.02191  1.00     
##           scale(strengthIn)  0.0055609 0.07457  0.24 0.24
##  Residual                    0.7270389 0.85267           
## Number of obs: 1975, groups:  subID, 209
## 
## Fixed effects:
##                      Estimate Std. Error         df t value Pr(>|t|)   
## (Intercept)         2.239e-01  7.090e-02  2.030e+02   3.158  0.00183 **
## scale(strengthOut)  6.527e-02  2.299e-02  4.132e+02   2.840  0.00474 **
## scale(strengthIn)   7.710e-03  2.789e-02  2.932e+00   0.276  0.80054   
## numID              -1.265e-02  4.571e-03  1.580e+02  -2.767  0.00633 **
## scale(length)       6.804e-02  2.257e-02  1.890e+03   3.015  0.00261 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(O) scl(I) numID 
## scl(strngO)  0.125                     
## scl(strngI)  0.135 -0.107              
## numID       -0.782 -0.044 -0.060       
## scal(lngth) -0.064 -0.036  0.152  0.030
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

Distinctness

Experiences with more causes are perceived as more distinct/different.

m<-lmer( scale(Dist) ~  scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## scale(Dist) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +  
##     (scale(outdegree) + scale(indegree) | subID)
##    Data: fullData
## 
## REML criterion at convergence: 5135.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9072 -0.5359  0.1170  0.6383  2.6875 
## 
## Random effects:
##  Groups   Name             Variance  Std.Dev. Corr       
##  subID    (Intercept)      0.2933625 0.54163             
##           scale(outdegree) 0.0256675 0.16021  -0.38      
##           scale(indegree)  0.0002987 0.01728  -0.55 -0.56
##  Residual                  0.6454951 0.80343             
## Number of obs: 1985, groups:  subID, 209
## 
## Fixed effects:
##                    Estimate Std. Error         df t value Pr(>|t|)   
## (Intercept)       1.726e-01  7.529e-02  2.591e+02   2.292  0.02270 * 
## scale(outdegree)  8.732e-02  2.912e-02  8.263e+01   2.999  0.00358 **
## scale(indegree)   3.735e-02  2.118e-02  6.440e+02   1.763  0.07837 . 
## numID            -9.835e-03  4.891e-03  1.879e+02  -2.011  0.04577 * 
## scale(length)    -8.188e-04  2.192e-02  1.945e+03  -0.037  0.97020   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(t) scl(n) numID 
## scale(tdgr)  0.189                     
## scale(ndgr)  0.117 -0.125              
## numID       -0.806 -0.264 -0.151       
## scal(lngth) -0.087 -0.081  0.142  0.056
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m<-lmer( scale(Dist) ~  scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Dist) ~ scale(strengthOut) + scale(strengthIn) + numID +  
##     scale(length) + (scale(strengthOut) + scale(strengthIn) |      subID)
##    Data: fullData
## 
## REML criterion at convergence: 5121.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8859 -0.5442  0.1147  0.6225  2.6721 
## 
## Random effects:
##  Groups   Name               Variance Std.Dev. Corr       
##  subID    (Intercept)        0.291166 0.53960             
##           scale(strengthOut) 0.019970 0.14132  -0.53      
##           scale(strengthIn)  0.003752 0.06126   0.02  0.22
##  Residual                    0.641282 0.80080             
## Number of obs: 1985, groups:  subID, 209
## 
## Fixed effects:
##                      Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)         1.969e-01  7.513e-02  2.660e+02   2.621  0.00927 ** 
## scale(strengthOut)  1.358e-01  2.827e-02  5.135e+01   4.804 1.39e-05 ***
## scale(strengthIn)   1.568e-02  2.575e-02  1.906e+01   0.609  0.54975    
## numID              -1.147e-02  4.844e-03  1.927e+02  -2.367  0.01894 *  
## scale(length)      -6.898e-04  2.178e-02  1.930e+03  -0.032  0.97474    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(O) scl(I) numID 
## scl(strngO)  0.207                     
## scl(strngI)  0.120 -0.078              
## numID       -0.805 -0.319 -0.085       
## scal(lngth) -0.079 -0.063  0.139  0.050

Do people higher in self-esteem have more self than other focused memories?

fullData$SminO <- fullData$SO_1 - fullData$SO_2

m<-lmer( SminO ~ SE + ( 1 | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SminO ~ SE + (1 | subID)
##    Data: fullData
## 
## REML criterion at convergence: 17813.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8301 -0.5846 -0.1584  0.6071  2.8011 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept)  287     16.94   
##  Residual             1191     34.50   
## Number of obs: 1776, groups:  subID, 206
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)   
## (Intercept)  20.1316     6.8611 197.7275   2.934  0.00374 **
## SE            0.4568     2.9956 200.2941   0.152  0.87896   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##    (Intr)
## SE -0.974

Page Rank, Hub and Changeability

m<-lmer(Chan ~  page + ( page | subID), data=fullData)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00201605 (tol = 0.002, component 1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ page + (page | subID)
##    Data: fullData
## 
## REML criterion at convergence: 7451.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5506 -0.5188  0.1435  0.6380  2.4692 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr 
##  subID    (Intercept) 0.7239   0.8508        
##           page        2.8202   1.6794   -0.37
##  Residual             1.8739   1.3689        
## Number of obs: 2067, groups:  subID, 211
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   5.28397    0.09343 139.75154  56.555   <2e-16 ***
## page          0.53139    0.39896 155.86021   1.332    0.185    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##      (Intr)
## page -0.686
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00201605 (tol = 0.002, component 1)
m<-lmer(Chan ~  pageW + ( pageW | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ pageW + (pageW | subID)
##    Data: fullData
## 
## REML criterion at convergence: 7450
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5729 -0.5228  0.1492  0.6423  2.4633 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr 
##  subID    (Intercept) 0.7045   0.8393        
##           pageW       2.1633   1.4708   -0.31
##  Residual             1.8739   1.3689        
## Number of obs: 2067, groups:  subID, 211
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   5.25817    0.09159 146.51342   57.41   <2e-16 ***
## pageW         0.68403    0.38436 163.86429    1.78    0.077 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##       (Intr)
## pageW -0.669
m<-lmer(Chan ~  pageOut + ( pageOut | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ pageOut + (pageOut | subID)
##    Data: fullData
## 
## REML criterion at convergence: 7423
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5889 -0.5275  0.1288  0.6339  2.2266 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr 
##  subID    (Intercept) 0.9007   0.949         
##           pageOut     5.2869   2.299    -0.70
##  Residual             1.8388   1.356         
## Number of obs: 2067, groups:  subID, 211
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   5.18192    0.09275 168.21829  55.872  < 2e-16 ***
## pageOut       1.32770    0.34736 165.28688   3.822 0.000187 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr)
## pageOut -0.705
m<-lmer(Chan ~  pageOutW + ( pageOutW | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ pageOutW + (pageOutW | subID)
##    Data: fullData
## 
## REML criterion at convergence: 7424.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5684 -0.5271  0.1284  0.6323  2.2400 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr 
##  subID    (Intercept) 0.8643   0.9297        
##           pageOutW    4.8983   2.2132   -0.65
##  Residual             1.8400   1.3565        
## Number of obs: 2067, groups:  subID, 211
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   5.16388    0.09199 168.14558  56.134  < 2e-16 ***
## pageOutW      1.40907    0.35061 164.58161   4.019 8.88e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## pageOutW -0.692
m<-lmer(Chan ~  hub + ( hub | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ hub + (hub | subID)
##    Data: fullData
## 
## REML criterion at convergence: 7374.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6191 -0.5154  0.1350  0.6102  2.6256 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr 
##  subID    (Intercept) 0.8484   0.9211        
##           hub         0.6631   0.8143   -0.56
##  Residual             1.7554   1.3249        
## Number of obs: 2067, groups:  subID, 211
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   5.14426    0.08313 180.94385  61.881  < 2e-16 ***
## hub           0.63695    0.10737 199.75811   5.932  1.3e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##     (Intr)
## hub -0.601
m<-lmer(Chan ~  hubW + ( hubW | subID), data=fullData)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ hubW + (hubW | subID)
##    Data: fullData
## 
## REML criterion at convergence: 7370.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6127 -0.5366  0.1385  0.6163  2.6130 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr 
##  subID    (Intercept) 0.8089   0.8994        
##           hubW        0.5306   0.7284   -0.53
##  Residual             1.7619   1.3274        
## Number of obs: 2067, groups:  subID, 211
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   5.15300    0.07975 188.71394  64.614  < 2e-16 ***
## hubW          0.69360    0.10529 232.85966   6.588 2.95e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##      (Intr)
## hubW -0.548
fullShort <- do.call(data.frame,                      # Replace Inf in data by NA
                   lapply(fullShort,
                          function(x) replace(x, is.infinite(x), NA)))
corMat <- fullShort %>% select(edgeTot:NFC) %>% cor(fullShort,use="pairwise.complete.obs")

outphm <- pheatmap(corMat, fontsize_row = 6, fontsize_col = 6, angle_col = 45, angle_row =45, width=100, height = 200 )

heatmaply_cor(round(corMat,3), Rowv=outphm[[1]], Colv=outphm[[2]], revC=TRUE, fontsize_row = 2.5, fontsize_col = 2.5, angle_col = 45, angle_row =45,  limits = c(-1, 1), colors = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100) )
fullShort %>% select(vad_compAg, MAIA:NFC) %>% corToOne(., "vad_compAg")
## Loading required package: corrr
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(referenceVar)` instead of `referenceVar` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
fullShort %>% select(vad_compAg, MAIA:NFC) %>% plotCorToOne(., "vad_compAg")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(edgeTot, MAIA:NFC) %>% corToOne(., "edgeTot")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(edgeTot, MAIA:NFC) %>% plotCorToOne(., "edgeTot")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(numID, MAIA:NFC) %>% corToOne(., "numID")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(numID, MAIA:NFC) %>% plotCorToOne(., "numID")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(dense, MAIA:NFC) %>% corToOne(., "dense")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(dense, MAIA:NFC) %>% plotCorToOne(., "dense")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(aveDist, MAIA:NFC) %>% corToOne(., "aveDist")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(aveDist, MAIA:NFC) %>% plotCorToOne(., "aveDist")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(Val_1_Homoph, MAIA:NFC) %>% corToOne(., "Val_1_Homoph")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Val_1_Homoph, MAIA:NFC) %>% plotCorToOne(., "Val_1_Homoph")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(Val_2_Homoph, MAIA:NFC) %>% corToOne(., "Val_2_Homoph")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Val_2_Homoph, MAIA:NFC) %>% plotCorToOne(., "Val_2_Homoph")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(Fund_Homoph, MAIA:NFC) %>% corToOne(., "Fund_Homoph")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Fund_Homoph, MAIA:NFC) %>% plotCorToOne(., "Fund_Homoph")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(Rep_Homoph, MAIA:NFC) %>% corToOne(., "Rep_Homoph")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Rep_Homoph, MAIA:NFC) %>% plotCorToOne(., "Rep_Homoph")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(Chan_Homoph, MAIA:NFC) %>% corToOne(., "Chan_Homoph")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Chan_Homoph, MAIA:NFC) %>% plotCorToOne(., "Chan_Homoph")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(recip, MAIA:NFC) %>% corToOne(., "recip")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(recip, MAIA:NFC) %>% plotCorToOne(., "recip")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

Cohesive

fullShort %>% select(cohes, MAIA:NFC) %>% corToOne(., "cohes")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(cohes, MAIA:NFC) %>% plotCorToOne(., "cohes")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(modular, MAIA:NFC) %>% corToOne(., "modular")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(modular, MAIA:NFC) %>% plotCorToOne(., "modular")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

Reciprocity

fullShort %>% select(recip, MAIA:NFC) %>% corToOne(., "recip")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(recip, MAIA:NFC) %>% plotCorToOne(., "recip")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

Standard Deviation of Degrees

fullShort %>% select(sdDeg, MAIA:NFC) %>% corToOne(., "sdDeg")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(sdDeg, MAIA:NFC) %>% plotCorToOne(., "sdDeg")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(sdDegW, MAIA:NFC) %>% corToOne(., "sdDegW")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(sdDegW, MAIA:NFC) %>% plotCorToOne(., "sdDegW")
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'